Sorting　is　an　important　construction　waste　management　tool　to　increase　recycling　rates　and　reduce　pollution.　Previous　studies　have　used　robots　to　improve　the　efficiency　of　construction　waste　recycling.　However,　in　large　construction　sites,　it　is　difficult　for　a　single　robot　to　accomplish　the　task　quickly,　and　multiple　robots　working　together　are　a　better　option.　Most　construction　waste　recycling　robotic　systems　are　developed　based　on　a　client-server　framework,　which　means　that　all　robots　need　to　be　continuously　connected　to　their　respective　cloud　servers.　Such　systems　are　low　in　robustness　in　complex　environments　and　waste　a　lot　of　computational　resources.　Therefore,　in　this　paper,　we　propose　a　pixel-level　automatic　construction　waste　recognition　platform　with　high　robustness　and　low　computational　resource　requirements　by　combining　multiple　computer　vision　technologies　with　edge　computing　and　cloud　computing　platforms.　Experiments　show　that　the　computing　platform　proposed　in　this　study　can　achieve　a　recognition　speed　of　23.3　fps　and　a　recognition　accuracy　of　90.81%　at　the　edge　computing　platform　without　the　help　of　network　and　cloud　servers.　This　is　23　times　faster　than　the　algorithm　used　in　previous　research.　Meanwhile,　the　computing　platform　proposed　in　this　study　achieves　93.2%　instance　segmentation　accuracy　on　the　cloud　server　side.　Notably,　this　system　allows　multiple　robots　to　operate　simultaneously　at　the　same　construction　site　using　only　a　single　server　without　compromising　efficiency,　which　significantly　reduces　costs　and　promotes　the　adoption　of　automated　construction　waste　recycling　robots.